Machine Learning Algorithms

Machine Learning Algorithmspadding: 0px; text rendering: optimizelegibility; font kerning: normal; text size adjust: 100%; box sizing: border box; font family: Georgia, "Droid Serif", Times, serif; font size: 16px; background color: rgb(255, 255, 255); margin top: 0. 75em ! important; margin bottom: 1. 25em ! important; line height: 1. 5em ! important;'Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive

padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'About This Book
Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
Get a solid foundation for your entry into Machine Learning strengthening your roots (algorithms) with this comprehensive guide.
'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'Who This Book Is For'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'What You Will Learn
Acquaint yourself with important elements of Machine Learning
Understand the feature selection and feature engineering process
Assess performance and error trade-offs for Linear Regression
Build a data model and understand how it works using different types of algorithm
Learn to tune the parameters of Support Vector machines
Implement clusters to a dataset
Explore the concept of Natural Processing Language and Recommendation Systems
Create a ML architecture from scratch.
'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'In Detail'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.'padding: 0px; text-rendering: optimizelegibility; font-kerning: normal; text-size-adjust: 100%; box-sizing: border-box; font-family: Georgia, "Droid Serif", Times, serif; font-size: 16px; background-color: rgb(255, 255, 255); margin-top: 0.75em !important; margin-bottom: 1.25em !important; line-height: 1.5em !important;'In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.